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Data-Efficient Design Exploration through Surrogate-Assisted Illumination
Evolutionary Computation ( IF 6.8 ) Pub Date : 2018-09-01 , DOI: 10.1162/evco_a_00231
Adam Gaier 1 , Alexander Asteroth 2 , Jean-Baptiste Mouret 3
Affiliation  

Design optimization techniques are often used at the beginning of the design process to explore the space of possible designs. In these domains illumination algorithms, such as MAP-Elites, are promising alternatives to classic optimization algorithms because they produce diverse, high-quality solutions in a single run, instead of only a single near-optimal solution. Unfortunately, these algorithms currently require a large number of function evaluations, limiting their applicability. In this article, we introduce a new illumination algorithm, Surrogate-Assisted Illumination (SAIL), that leverages surrogate modeling techniques to create a map of the design space according to user-defined features while minimizing the number of fitness evaluations. On a two-dimensional airfoil optimization problem, SAIL produces hundreds of diverse but high-performing designs with several orders of magnitude fewer evaluations than MAP-Elites or CMA-ES. We demonstrate that SAIL is also capable of producing maps of high-performing designs in realistic three-dimensional aerodynamic tasks with an accurate flow simulation. Data-efficient design exploration with SAIL can help designers understand what is possible, beyond what is optimal, by considering more than pure objective-based optimization.

中文翻译:

通过替代辅助照明进行数据高效设计探索

设计优化技术通常用于设计过程的开始,以探索可能的设计空间。在这些领域中,诸如 MAP-Elite 之类的光照算法是经典优化算法的有前途的替代方案,因为它们可以在一次运行中生成多样化、高质量的解决方案,而不仅仅是一个接近最优的解决方案。不幸的是,这些算法目前需要大量的函数评估,限制了它们的适用性。在本文中,我们介绍了一种新的照明算法,即代理辅助照明 (SAIL),它利用代理建模技术根据用户定义的特征创建设计空间的地图,同时最大限度地减少适应度评估的数量。关于二维翼型优化问题,SAIL 产生了数百种不同但高性能的设计,其评估次数比 MAP-Elites 或 CMA-ES 少几个数量级。我们证明,SAIL 还能够通过精确的流动模拟在逼真的三维空气动力学任务中生成高性能设计的地图。使用 SAIL 进行数据高效的设计探索可以帮助设计人员了解什么是可能的,超越什么是最佳的,考虑的不仅仅是基于目标的优化。
更新日期:2018-09-01
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